It’s no secret that the tender industry has long been rife with inefficiencies and bottlenecks. Data entry errors, fragmented communication, inadequate tracking—you name it.
Fortunately, the technological advances of recent years have been gradually paving a new way for tender management and changing the way companies optimize processes, improve collaboration, and elevate decision-making.
In this article, we’ll take a closer look at the biggest challenges the tender management industry is facing today and how technology is bound to tackle these in the coming years. Whether you are a procurement professional, a bidder, or a stakeholder, having a strong grasp of these trends will help you to remain competitive in the market.
Current challenges in tender management
Several problems define modern-day tender management, namely, manual processes and the inefficiencies associated with them, lack of transparency and visibility, lack of centralized information, and, finally, inefficient communication and collaboration. Let’s take a closer look at each of these.
Tender management stumbling blocks:
⦾ Manual processes and the associated inefficiencies
⦾ Lack of transparency and visibility
⦾ Dispersed information
⦾ Inefficient communication and collaboration.
Manual processes and inefficiencies
It’s normal for manual processes to result in inefficiencies and errors and, as a result, extended tendering periods. Certain 2024 reports about global procurement trends suggest that 56% of procurement professionals cite the inefficiencies of manual procurement processes as a major challenge.
It’s also essential to acknowledge that data entry itself is quite a repetitive and tedious task that has a significant adverse impact on bid teams in terms of morale and productivity, which, in turn, leads to frustration and dwindling job satisfaction.
Lack of transparency and visibility
There’s a broad range of issues that come with an inherent lack of transparency in the tendering process, and they can create an environment that is ripe for non-compliance with laws and regulations.
There’s solid, empirical evidence to confirm that this lack of transparency often has a negative effect on competitive tendering environments. It weakens the consensus on regulations and increases the likelihood of rule violations, irresponsible bids, conflicts of interest, collusion, bribery, and corruption.
Lack of centralized information and documentation
The fairly widespread lack of centralized information in tender management creates a wide range of hurdles for organizations, such as increased errors in data, slow or delayed decision-making, and inefficient communication, to name just a few.
As a result, this leads to lost opportunities and regular miscommunication between vendors. This lack of centralization puts pressure on procurement tracking, reduces transparency, and makes compliance with regulations unnecessarily complicated.
Inefficient communication and collaboration
Inefficient communication is also rather pervasive in tender management; it comes as a consequence of the untransparent and uncentralized environment mentioned above, which is rife with a whole array of inefficiencies.
As a result, there is an overreliance on fragmented communication such as email and phone calls that can cause near-permanent delays. This often results in longer tender cycles and increased costs since bidding teams have to invest more resources into addressing inefficiencies instead of focusing on strategic objectives.
Current trends in procurement
Modern-day procurement is being significantly shaped by digital transformation and a variety of artificial intelligence (AI) features such as natural language processing, machine learning (ML), and others.
The prompt adoption of e-tendering platforms like Tenderwell is an important part of how organizations are streamlining their tender management processes, reducing manual errors, and improving overall efficiency.
Digital transformation in tender management
Digital transformation has quickly improved multiple areas of tender management in the form of e-tendering platforms and introduced a whole gamut of automation tools. These make navigating and processing information much more efficient, while also considerably reducing manual errors and the resources needed to execute administrative tasks.
More importantly, access to data provides vendors with the opportunity to improve decision-making due to access to a broad range of data via digital transformation initiatives. According to McKinsey & Company, organizations that leverage data-driven B2B sales growth engines have seen above-market growth and EBITDA of 15-25%.
However, the really important metric here is the time saved on monitoring and qualifying for tender opportunities, which can be significantly cut by implementing digital innovations.
Read more on the topic: How to win government contracts: a step-by-step guide
AI and ML in tender management
AI and ML are really moving the needle in tender management by significantly contributing to efficiency, decision-making, and system integrity.
Constantly improving and becoming more accessible, AI tools now allow organizations to automate many tasks, including identifying tenders, processing bid documentation, and crafting proposals, which, in turn, inevitably leads to saved time and resources.
⦾ AI reduces errors by proofreading the content and ensuring adherence to approval processes, as well as improving the accuracy of content.
⦾ It provides data-driven insights by analyzing historical data to identify successful tender types, competitors, and pricing strategies.
⦾ It also allows organizations to evaluate bids by analyzing vast amounts of data to identify patterns and rank bids based on specific criteria.
Having access to a huge amount of data also allows organizations to leverage predictive analytics to allow them to have a better understanding of the forecasts and trends, which enables them to better manage their risks and strategies.
Machine learning is a subfield of AI that has contributed to predictive analytics in tender management. It allows detailed forecasts about the potential demand or risks of upcoming timeframes to be made, which enables organizations to take a more proactive approach towards their strategies and resource allocation.
By leveraging historical data, ML models can forecast trends in procurement processes, such as future demand or potential risks. Furthermore, ML enhances supplier intelligence by assessing supplier performance and risk profiles, which allows companies to build more resilient supply chains.
Wrap-up
Tender management is currently going through its most transformative period, defined by the incorporation of digital tech and AI. It’s highly likely that the longstanding challenges associated with fragmented and inefficient communication, as well as the inefficiencies caused by manual data entry, will soon be history thanks to platforms like Tenderwell and the suite of digital instruments that accompany it.